MCScheduling 1.0
Set of Algorithms for Solving Mixed-Criticality Scheduling
Packages | Classes
Package MCScheduling.MixedCriticality

Contains the representation of Mixed-Criticality Job, Mixed-Criticality Instance, several methods for their random generation, and mainly few algorithms to solve Mixed-Criticality Scheduling Problem. More...

Packages

package  CEDF
 

Contains the implementation of a mixed-criticality solver based on the clairvoyant non-preemptive earliest deadline first algorithm.


package  DP
 

Contains the implementation of a mixed-criticality solver based on the dynamic programming algorithm (similar to the one solving the traveling salesman problem).


package  GA
 

Contains the implementation of a mixed-criticality solver based on the genetic algorithm.


package  MIP
 

Contains the implementation of a mixed-criticality solver based on the branch and bound algorithm provided by Gurobi 4.5 library.


package  SA
 

Contains the implementation of a mixed-criticality solver based on the simulated annealing algorithm.


Classes

class  CBaseMixedCriticalitySolver
 The basic implementation of the IMixedCriticalitySolver. More...
class  CConsoleSolver
 The command line interface for the MCScheduling application. More...
class  CDefaultMixedCriticalityInstanceGenerator
 The default implemenation of the IMixedCriticalityInstanceGenerator interface. More...
class  CMixedCriticalityInstance
 Representation of a mixed-criticality instance. More...
class  CMixedCriticalityInstancesSerializer
 CMixedCriticalityInstancesSerializer provides a methods to serialize and deserialize mixed-criticality instances. More...
class  CMixedCriticalityJob
 A CJob instance represents a mixed-criticality job. More...
class  CRandomInstanceGenerator
 Generates a list of random MC instances. More...
class  CWorstCaseMixedCriticalityInstanceGenerator
 Generates a random "worst-case" MC instance. More...
interface  IMixedCriticalitySolver
 An interface for an algorithn solving mixed-criticality instances. More...

Detailed Description

Contains the representation of Mixed-Criticality Job, Mixed-Criticality Instance, several methods for their random generation, and mainly few algorithms to solve Mixed-Criticality Scheduling Problem.

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